index.md

layout	default
title	Deep Learning @ VU

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1. Introduction

2. Backpropagation

3. Convolutions

4. Tools of the trade

5. Sequences

6. Latent variable models

7. Unsupervised representation learning

8. Graphs

9. Self-attention

10. Reinforcement learning

11. Diffusion

12. Generalization

13. Explainability

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Content

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	Lecture	Lecturer	Links
w1	1. Introduction 1.1 Neural networks 1.2 Classification and regression 1.3 Autoencoders	Peter Bloem	playlist pdf
	2. Backpropagation 2.1 Scalar backpropagation 2.2 Tensor backpropagation 2.3 Automatic differentiation 2.4 Tensor details*	Peter Bloem	playlist pdf
	3. Convolutions 3.1 Introduction 3.2 Conv1D (a) 3.3 Conv1D (b) 3.3 Conv2D, Conv3D, ConvND	Michel Cochez	playlist pdf
w2	3. Tools of the trade 4.1 Deep learning in practice 4.2 Why does any of this work at all? 4.3 Understanding optimizers 4.4 The bag of tricks	Peter Bloem	playlist pdf
	5. Sequences 5.1 Learning from sequences 5.2 Recurrent neural networks 5.3 LSTMs and friends 5.4 CNNs for sequential data 5.5 ELMo, a case study	Peter Bloem, David Romero	playlist pdf
w3	6. Latent Variable Models 6.1 Why Generative Modeling 6.2 Autoencoders 6.3 Variational Autoencoders	Shujian Yu	pdf
	7. Unsupervised representation learning 7.0 Introduction 7.1 VAE Implementation 7.2 KL Divergence 7.3 MMD-VAE	Shujian Yu	pdf
w4	8. Learning with graphs 8.1 Introduction - Graphs (1A) 8.2 Introduction - Embeddings (1B) 8.3 Graph Embedding Techniques 8.4 Graph Neural Networks 8.5 Query embedding	Michael Cochez	playlist pdf
	9. Transformers and self-attention 9.1 Self-attention 9.2 Transformers 9.3 Famous transformers 9.4 Scaling up	Peter Bloem	playlist pdf
w5	10. Reinforcement learning	Vincent Francois-Lavet	pdf
w6	11. Diffusion models 11.1 Naive diffusion 11.2 Understanding Gaussians 11.3 Gaussian diffusion	Peter Bloem	playlist pdf
	12. Generalization 12.0 Introduction 12.1 Review 12.2 Problem 12.3 Generalization Bound	Shujian Yu	pdf

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Last year's content

		lecturer	videos	slides
week 1	Introduction	Jakub Tomczak	A, B, C, D	pdf
	Backpropagation	Peter Bloem	A, B, C, D	pdf
	Convolutional Neural Networks	Michael Cochez	A, B, C, D	pdf
week 2	Sequential data	Peter Bloem	A, B, C, D, E*	pdf
	Tools of the trade	Peter Bloem	A, B, C, D	pdf
week 3	Latent Variable Models (pPCA and VAE)	Jakub Tomczak	A, B, C	pdf
	GANs	Jakub Tomczak	A, B	pdf
week 4	Learning with Graphs	Michael Cochez	playlist	pdf
	Transformers & self-attention	Peter Bloem	A, B, C	pdf
week 5	Reinforcement learning	Emile van Krieken	A, B, C	pdf
	Reinforcement learning (extra)	Emile van Krieken	A, B, C	pdf
	Autoregressive and Flow-based models	Jakub Tomczak	A, B, C	pdf